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Related Concept Videos

Precipitation Processes01:12

Precipitation Processes

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The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
518
Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

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Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
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Precipitation Gravimetry01:03

Precipitation Gravimetry

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Precipitation gravimetry is based on converting an analyte into a sparingly soluble precipitate, which is separated by filtration and weighed. An ideal precipitate should be pure, insoluble, of known composition, and easily filtered from the reaction mixture.
In determining nickel by gravimetric analysis, a precipitant of ethanolic dimethylglyoxime is added to a hot nickel salt solution. This is quickly followed by the dropwise addition of dilute ammonia solution until precipitation occurs. A...
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Precipitation Titration: Endpoint Detection Methods01:19

Precipitation Titration: Endpoint Detection Methods

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In argentometric precipitation titrations, endpoints can be detected visually by the Mohr, Volhard, and Fajans methods. In the Mohr method, adding a soluble chromate indicator gives an initial yellow color to the analyte solution. As the titrant is added, the first excess of silver ions forms a red silver chromate precipitate, marking the endpoint. The solution pH should be maintained at about 8 by adding solid CaCO3.
In the Volhard method, a standard excess of AgNO3 is first added to the...
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Types of Coprecipitation01:10

Types of Coprecipitation

716
Coprecipitation is the contamination of a precipitate by otherwise soluble species and occurs via different processes. In colloidal precipitates, coprecipitation occurs via surface adsorption. For instance, barium sulfate has a primary layer of adsorbed barium ions and a secondary layer of nitrate counterions. This results in contamination of the precipitate by barium nitrate.
Sometimes, ions in a crystal lattice can undergo isomorphous replacement by inclusions of similar charge and size. For...
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Classification of Systems-I01:26

Classification of Systems-I

241
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
241

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Rain Discrimination with Machine Learning Classifiers for Opportunistic Rain Detection System Using Satellite

Christian Gianoglio1, Ayham Alyosef1, Matteo Colli2

  • 1Department of Electrical, Electronics and Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, 16145 Genova, Italy.

Sensors (Basel, Switzerland)
|February 11, 2023
PubMed
Summary
This summary is machine-generated.

Accurate rainfall detection using oblique earth-space links (OELs) is crucial for disaster management. Neural networks (NN) effectively classify rainy periods, outperforming other machine learning models in this critical application.

Keywords:
machine learningoblique earth-space linkssatellite microwave linkssmart rainfall system

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Area of Science:

  • Environmental Science
  • Meteorology
  • Remote Sensing

Background:

  • Climate change intensifies extreme weather events, necessitating advanced disaster risk management systems.
  • Real-time monitoring and forecasting are vital for early warnings and hazard mitigation.
  • Oblique earth-space links (OELs) offer a promising method for real-time rainfall detection.

Purpose of the Study:

  • To address the challenge of classifying rainy and non-rainy periods using OEL data.
  • To compare the performance of different machine learning (ML) classifiers for rainfall event detection.
  • To evaluate the effectiveness of neural networks (NN) against support vector machines (SVM), random forest (RF), and decision trees (DT).

Main Methods:

  • Utilized data from eighteen rain events, correlating satellite-to-earth link quality with tipping bucket rain gauge (TBRG) measurements.
  • Preprocessed TBRG data and extracted feature sets (6 and 12 features) from microwave link data.
  • Applied and compared four ML classifiers: SVM, NN, RF, and DT.

Main Results:

  • The neural network (NN) classifier demonstrated superior performance in distinguishing between rainy and non-rainy periods.
  • Performance was evaluated across various data arrangements and feature set sizes.
  • NN consistently outperformed SVM, RF, and DT in the classification task.

Conclusions:

  • Neural networks are highly effective for classifying rainfall events using OEL data.
  • This research contributes to improving real-time rainfall monitoring systems for disaster preparedness.
  • Accurate classification of rainfall periods is a key step towards reliable rainfall intensity estimation via OELs.